The Relevance of the Application of the Neural Approaches in the Premature Detection of the Banking Difficulties: Case of Tunisia

Niazi KAMMOUN, Chanez CHARFI

Abstract

We propose in this study to test the relevance of the use of an early warning system (EWS) for banking difficulties in the Tunisian case. This model is based on the use of a multilayer neural network with a back-propagation algorithm. From a sample of 18 Tunisian banks, we try to establish the different ratios of the financial health of banks, extracted from CAMEL rating system. With a good ranking percentage obtained for the total sample of more than 93%, we conclude that neural networks are found to be robust for the prediction of the fragility of the Tunisian banks.

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